Intern vs pytest comparison of testing frameworks
What are the differences between Intern and pytest?

	Intern https://github.com/theintern/intern	pytest https://docs.pytest.org/en/latest/
Programming language	JavaScript	Python
Category	Unit Testing, Functional Testing	Unit Testing
General info	Intern is minimal test system for JavaScript designed to write and run consistent. Intern is a complete test system for JavaScript designed to help you write and run consistent, high-quality test cases for your JavaScript libraries and applications. Using Intern we can write tests in JavaScript and TypeScript using any style like TDD, and BDD. Intern can run unit tests in most browsers that support ECMAScript	Pytest is the TDD 'all in one' testing framework for Python Pytest is a powerful Python testing framework that can test all and levels of software. It is considered by many to be the best testing framework in Python with many projects on the internet having switched to it from other frameworks, including Mozilla and Dropbox. This is due to its many powerful features such as ‘assert‘ rewriting, a third-party plugin model and a powerful yet simple fixture model.
xUnit Set of frameworks originating from SUnit (Smalltalk's testing framework). They share similar structure and functionality.	No	No
Client-side Allows testing code execution on the client, such as a web browser	Yes Intern is a complete test system for JavaScript It Runs in the browser and can test any front-end component and functionality	Yes pytest can test any part of the stack including front-end components
Server-side Allows testing the bahovior of a server-side code	Yes Since it is a complete testing system that can test any type of JavaScript code, it can test server-side behaviour and components as well	Yes pytest is powerful enough to test database and server components and functionality
Fixtures Allows defining a fixed, specific states of data (fixtures) that are test-local. This ensures specific environment for a single test	N/A	Yes Pytest has a powerful yet simple fixture model that is unmatched in any other testing framework.
Group fixtures Allows defining a fixed, specific states of data for a group of tests (group-fixtures). This ensures specific environment for a given group of tests.	N/A	Yes Pytest's powerful fixture model allows grouping of fixtures
Generators Supports data generators for tests. Data generators generate input data for test. The test is then run for each input data produced in this way.		Yes pytest has a hook function called pytest_generate_tests hook which is called when collecting a test function and one can use it to generate data
Licence Licence type governing the use and redistribution of the software	FreeBSD License	MIT License
Mocks Mocks are objects that simulate the behavior of real objects. Using mocks allows testing some part of the code in isolation (with other parts mocked when needed)	Intern uses the Dojo Toolkit’s AMD loader. To mock, you should be able to just use the standard AMD 'map' feature, else you can use third party libraries like sinon.js	Yes By either using unittest.mock or using pytest-mock a thin wrapper that provides mock functionality for pytest
Grouping Allows organizing tests in groups	Yes You can group tests into Suites which may be specified as file paths or using glob expressions, there is typically one top-level suite per module.	Yes Tests can be grouped with pytest by use of markers which are applied to various tests and one can run tests with the marker applied
Other Other useful information about the testing framework